#include <iostream>
#include <opencv2/core/core.hpp>//opencv核心模块
#include <opencv2/features2d/features2d.hpp>//opencv特征点
#include <opencv2/highgui/highgui.hpp>//opencv gui模块
#include <opencv2/calib3d/calib3d.hpp>
#include <chrono>
// #include "extra.h" // use this if in OpenCV2

using namespace std;
using namespace cv;

/****************************************************
 * 本程序演示了如何使用2D-2D的特征匹配估计相机运动
 * **************************************************/

void find_feature_matches(
        const Mat &img_1, const Mat &img_2,
        std::vector<KeyPoint> &keypoints_1,
        std::vector<KeyPoint> &keypoints_2,
        std::vector<DMatch> &matches);//定义find_feature_matches函数 输入图像1和图像2，输出特征点集合1、特征点集合2和匹配点对

void pose_estimation_2d2d(
        std::vector<KeyPoint> keypoints_1,
        std::vector<KeyPoint> keypoints_2,
        std::vector<DMatch> matches,
        Mat &R, Mat &t);//定义pose_estimation_2d2d 输入特征点集合1、特征点集合2和匹配点对，输出估计的旋转矩阵、估计的平移向量和本质矩阵，平移向量差了一个尺度因子(可详细阅读书上p175-p176)
//R,t表示旋转矩阵和平移

// 像素坐标转相机归一化坐标
Point2d pixel2cam(const Point2d &p, const Mat &K);//输出一个像素点和相机内参矩阵，输出该像素点在归一化平面上的坐标

int main(int argc, char **argv) {
    if (argc != 3) {
        cout << "usage: pose_estimation_2d2d img1 img2" << endl;//输出命令行用法
        return 1;
    }
    chrono::steady_clock::time_point t1 = chrono::steady_clock::now();  //计时开始
    //-- 读取图像
    Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);//读取图像1  CV_LOAD_IMAGE_COLOR表示返回一张彩色图像
    Mat img_2 = imread(argv[2], CV_LOAD_IMAGE_COLOR);//读取图像2  CV_LOAD_IMAGE_COLOR表示返回一张彩色图像
    assert(img_1.data && img_2.data && "Can not load images!");//assert()为断言函数，条件为假则停止执行

    //特征提取和特征匹配
    vector<KeyPoint> keypoints_1, keypoints_2;//特征点1 -> 图像1 特征点2 -> 图像2
    vector<DMatch> matches;//匹配 matches
    find_feature_matches(img_1, img_2, keypoints_1, keypoints_2, matches);//调用find_feature_matches函数
    cout << "一共找到了" << matches.size() << "组匹配点" << endl;//输出匹配点数

    //-- 估计两张图像间运动
    Mat R, t;//R,t表示旋转矩阵和平移
    pose_estimation_2d2d(keypoints_1, keypoints_2, matches, R, t);//调用函数

    //-- 验证E=t^R*scale
    Mat t_x =
            (Mat_<double>(3, 3) << 0, -t.at<double>(2, 0), t.at<double>(1, 0),
                    t.at<double>(2, 0), 0, -t.at<double>(0, 0),
                    -t.at<double>(1, 0), t.at<double>(0, 0), 0);//Mat_<double>(3, 3) <<（）表示opencv中的初始化方法 即对 t_x进行初始化

    cout << "t^R=" << endl << t_x * R << endl;//输出t^R

    //-- 验证对极约束
    Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);//相机内参矩阵
    for(int i = 0; i < matches.size(); i++)
    {
        DMatch m = matches[i];
        Point2d pt1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);//像素坐标转相机归一化坐标
        Mat y1 = (Mat_<double>(3, 1) << pt1.x, pt1.y, 1);
        Point2d pt2 = pixel2cam(keypoints_2[m.trainIdx].pt, K);
        Mat y2 = (Mat_<double>(3, 1) << pt2.x, pt2.y, 1);
        Mat d = y2.t() * t_x * R * y1;//对极几何的残差，结果应该为标量0 d = y2(T) * t(^)* R * y1 	就是视觉slan十四讲p167的式7.8
        cout << "The " << i << " epipolar constraint（匹配点对的对极几何残差）： " << d << " ！" << endl;
    }
    // for (DMatch m: matches) //opencv DMatch(int queryIdx, int trainIdx, float distance)
    // {
    //   Point2d pt1 = pixel2cam(keypoints_1[m.queryIdx].pt, K);//像素坐标转相机归一化坐标
    //   Mat y1 = (Mat_<double>(3, 1) << pt1.x, pt1.y, 1);
    //   Point2d pt2 = pixel2cam(keypoints_2[m.trainIdx].pt, K);
    //   Mat y2 = (Mat_<double>(3, 1) << pt2.x, pt2.y, 1);
    //   Mat d = y2.t() * t_x * R * y1;//对极几何的残差，结果应该为标量0 d = y2(T) * t(^)* R * y1 	就是视觉slan十四讲p167的式7.8
    //   cout << "The" << i << "epipolar constraint（匹配点对的对极几何残差）： " << d << " ！" << endl;
    //   //cout << "epipolar constraint = " << d << endl;
    // }
    chrono::steady_clock::time_point t2 = chrono::steady_clock::now();
    chrono::duration<double> time_used = chrono::duration_cast<chrono::duration<double>> (t2- t1);
    cout << "执行程序所花的时间为" << time_used.count() << "秒！" << endl;
    return 0;
}

void find_feature_matches(const Mat &img_1, const Mat &img_2,
                          std::vector<KeyPoint> &keypoints_1,
                          std::vector<KeyPoint> &keypoints_2,
                          std::vector<DMatch> &matches) {
    //-- 初始化
    Mat descriptors_1, descriptors_2;//描述子1he描述子2
    // used in OpenCV3
    Ptr<FeatureDetector> detector = ORB::create();
    Ptr<DescriptorExtractor> descriptor = ORB::create();
    // use this if you are in OpenCV2
    // Ptr<FeatureDetector> detector = FeatureDetector::create ( "ORB" );
    // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create ( "ORB" );
    Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");//使用汉明距离进行特征匹配
    //-- 第一步:检测 Oriented FAST 角点位置
    detector->detect(img_1, keypoints_1);//检测图像1的 Oriented FAST 角点
    detector->detect(img_2, keypoints_2);//检测图像2的 Oriented FAST 角点

    //-- 第二步:根据角点位置计算 BRIEF 描述子
    descriptor->compute(img_1, keypoints_1, descriptors_1);
    descriptor->compute(img_2, keypoints_2, descriptors_2);

    //-- 第三步:对两幅图像中的BRIEF描述子进行匹配，使用 Hamming 距离
    vector<DMatch> match;
    //BFMatcher matcher ( NORM_HAMMING );
    matcher->match(descriptors_1, descriptors_2, match);

    //-- 第四步:匹配点对筛选
    double min_dist = 150, max_dist = 0; //求最小距离

    //找出所有匹配之间的最小距离和最大距离, 即是最相似的和最不相似的两组点之间的距离
    for (int i = 0; i < descriptors_1.rows; i++)
    {
        double dist = match[i].distance;
        if (dist < min_dist) min_dist = dist;
        if (dist > max_dist) max_dist = dist;
    }

    printf("-- Max dist : %f \n", max_dist);
    printf("-- Min dist : %f \n", min_dist);


    //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误.但有时候最小距离会非常小,设置一个经验值30作为下限.、
    //遍历所有匹配，排除误匹配
    for (int i = 0; i < descriptors_1.rows; i++)
    {
        if (match[i].distance <= max(2 * min_dist, 30.0))//不同的结果可以在这里设置
            //当描述子之间的距离大于两倍的最小距离时,即认为匹配有误 30.0为经验值
        {
            matches.push_back(match[i]);
        }
    }
    //显示匹配图
    Mat img_match;
    drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_match, Scalar::all(-1));
    imshow("good matches", img_match);
    waitKey(0);  //程序暂停执行，等待一个按键输入
}

Point2d pixel2cam(const Point2d &p, const Mat &K) {
    return Point2d
            (
                    (p.x - K.at<double>(0, 2)) / K.at<double>(0, 0),
                    (p.y - K.at<double>(1, 2)) / K.at<double>(1, 1)
            );
}

void pose_estimation_2d2d(std::vector<KeyPoint> keypoints_1,
                          std::vector<KeyPoint> keypoints_2,
                          std::vector<DMatch> matches,
                          Mat &R, Mat &t) {
    // 相机内参,TUM Freiburg2
    Mat K = (Mat_<double>(3, 3) << 520.9, 0, 325.1, 0, 521.0, 249.7, 0, 0, 1);

    //-- 把匹配点转换为vector<Point2f>的形式
    vector<Point2f> points1;
    vector<Point2f> points2;

    for (int i = 0; i < (int) matches.size(); i++) //编译所有匹配点
    {
        points1.push_back(keypoints_1[matches[i].queryIdx].pt);
        points2.push_back(keypoints_2[matches[i].trainIdx].pt);
    }

    //-- 计算基础矩阵
    Mat fundamental_matrix;
    fundamental_matrix = findFundamentalMat(points1, points2, CV_FM_8POINT);//采用8点法求解F = K(-T) * E * K(-1)
    cout << "fundamental_matrix is " << endl << fundamental_matrix << endl;//输出基础矩阵

    //-- 计算本质矩阵
    Point2d principal_point(325.1, 249.7);  //相机光心, TUM dataset标定值
    double focal_length = 521;      //相机焦距, TUM dataset标定值
    Mat essential_matrix;
    essential_matrix = findEssentialMat(points1, points2, focal_length, principal_point);//E = t(^) * R
    cout << "essential_matrix is " << endl << essential_matrix << endl;//输出本质矩阵

    //-- 计算单应矩阵
    //-- 但是本例中场景不是平面，单应矩阵意义不大
    Mat homography_matrix;
    homography_matrix = findHomography(points1, points2, RANSAC, 3);//H = K * (R - tn(T) / d) * K(-1)
    cout << "homography_matrix is " << endl << homography_matrix << endl;

    //-- 从本质矩阵中恢复旋转和平移信息.
    // 此函数仅在Opencv3中提供
    recoverPose(essential_matrix, points1, points2, R, t, focal_length, principal_point);
    cout << "R is " << endl << R << endl;//输出旋转矩阵
    cout << "t is " << endl << t << endl;//输出平移向量

}